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Purpose: To study the effect of pupil dilation on a biometric iris recognition (BIR) system for personal authentication and identification. Methods: A prospective, non?randomized, single?center cohort study was conducted on patients who reported for a routine eye check?up from November 2017 to November 2019 (2 years). An iris scanning device “IRITECH?MK2120U” was used to initially enroll the undilated eyes. Baseline scans were taken after matching with the enrolled database. All eyes were topically dilated and matched again with the enrolled database. The Hamming distance (a measure of disagreement between two iris codes) and recognition status were recorded from the device output, and eyes were evaluated by slit?lamp ophthalmoscopy with special emphasis on pupil shape, size, and texture. Results: All 321 enrolled eyes matched after topical dilation. The pupil size had a significant effect on Hamming distance with a P value <0.05. There were no false matches. A correct recognition rate of 100% was obtained after dilation. No loss of iris texture or pupil shape was observed after dilation. Conclusion: A BIR system is a reliable method for identification and personal authentication after pupil dilation. Topically dilated pupils are not a cause for non?recognition of iris scans.
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Objective To introduce iris recognition application in human assisted reproductive technology and set up an iris registration platform and an iris recognition platform for patients .Methods Every patient′s iris was used as the only code.The complicated verification of identity cards and birth permit was replaced by the digital service platform .We set up a database for basic information ,and electronic records of treatment and follow-up for centralized management .Results and Conclusion Compared to other authentication systems ,this iris digital system is easy and efficient .It can more effectively eliminate replacement and prevent medical disputes .The time taken to check the information of each patient is shortened to twenty seconds .
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To improve flexibility and reliability of iris recognition algorithm while keeping iris recognition success rate, an iris recognition approach for combining SVM with ICA feature extraction model is presented. SVM is a kind of classifier which has demonstrated high generalization capabilities in the object recognition problem. And ICA is a feature extraction technique which can be considered a generalization of principal component analysis. In this paper, ICA is used to generate a set of subsequences of feature vectors for iris feature extraction. Then each subsequence is classified using support vector machine sequence kernels. Experiments are made on CASIA iris database, the result indicates combination of SVM and ICA can improve iris recognition flexibility and reliability while keeping recognition success rate.
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To improve flexibility and reliability of iris recognition algorithm while keeping iris recognition success rate, an iris recognition approach for combining SVM with ICA feature extraction model is presented. SVM is a kind of classifier which has demonstrated high generalization capabilities in the object recognition problem. And ICA is a feature extraction technique which can be considered a generalization of principal component analysis. In this paper, ICA is used to generate a set of subsequences of feature vectors for iris feature extraction. Then each subsequence is classified using support vector machine sequence kernels. Experiments are made on CASIA iris database, the result indicates combination of SVM and ICA can improve iris recognition flexibility and reliability while keeping recognition success rate.